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    Weifang Medical University Reports Findings in Cerebral Hemorrhage (Machine lear ning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis)

    39-39页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Cerebral Hemorrhage is the subject of a report. Accor ding to news reporting out of Weifang, People's Republic of China, by NewsRx edi tors, research stated, "Early identification of hematoma enlargement and persist ent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinic ally effective tools, radiomics has been gradually introduced into the early ide ntification of hematoma enlargement." Our news journalists obtained a quote from the research from Weifang Medical Uni versity, "Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to ex plore the value of radiomics in the early detection of HE in patients with cereb ral hemorrhage. Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles a re considered eligible. The radiomics quality scoring (RQS) tool was used to eva luate included studies. A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical featur es. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical mo dels (0.69 C-index in the training cohort and 0.70 C-index in the validation coh ort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts."
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    Lanzhou University Reports Findings in Artificial Intelligence (Embedded monitor ing system and teaching of artificial intelligence online drug component recogni tion)

    40-41页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Lanzhou, People 's Republic of China, by NewsRx editors, research stated, "Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market an d ensure drug safety." Our news journalists obtained a quote from the research from Lanzhou University, "The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the us e of the monitoring system. With the continuous development of computer and meas urement technologies, various biochemical data are increasing at an unprecedente d speed, and numerous databases are emerging. Extracting patterns from considera ble known data and experimental facts is an essential task for a wide range of b iological and chemical workers. Pattern recognition is one of the essential tech nologies for data mining. It is widely used in industry, agriculture, national d efense, biomedicine, meteorology, astronomy, and other fields. To improve the ef fect of the online drug ingredient recognition system, this study used AI to des ign an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study cons tructed the framework of the AI online drug ingredient recognition-embedded moni toring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern reco gnition system, and presented the pattern recognition algorithm and the algorith m evaluation index. Afterward, it used pattern recognition to conduct a qualitat ive analysis of the infrared spectrum of drug components and introduced the over all process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universitie s, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensiti vity, specificity, and Matthew correlation coefficient of the online drug compon ent recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85."
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    Hospital Arnau de Vilanova Reports Findings in Atrial Fibrillation (Oral anticoa gulant treatment in atrial fibrillation: The AFIRMA real-world study using natur al language processing and machine learning)

    41-42页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Heart Disorders and Di seases - Atrial Fibrillation is the subject of a report. According to news repor ting out of Valencia, Spain, by NewsRx editors, research stated, "Oral anticoagu lation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain la cks substantial real-world evidence. We aimed to analyze the prevalence, clinica l characteristics, and treatment patterns among patients with AF undertaking OAC , using natural language processing (NLP) and machine learning (ML)." Our news journalists obtained a quote from the research from Hospital Arnau de V ilanova, "This retrospective study included AF patients on OAC from 15 Spanish h ospitals (2014-2020). Using EHRead®(including NLP and ML), and SNOMED_ CT, we extracted and analyzed patient demographics, comorbidities, and OAC treat ment from electronic health records. AF prevalence was estimated, and a descript ive analysis was conducted. Among 4,664,224 patients in our cohort, AF prevalenc e ranged from 1.9% to 2.9%. A total of 57,190 patient s on OAC therapy were included, 80.7% receiving Vitamin K antagoni sts (VKA) and 19.3% Direct-acting OAC (DOAC). The median age was 7 8 and 76 years respectively, with males constituting 53% of the co hort. Comorbidities like hypertension (76.3%), diabetes (48.0% ), heart failure (42.2%), and renal disease (18.7%) we re common, and more frequent in VKA users. Over 50% had a high CHA 2DS2-VASc score. The most frequent treatment switch was from DOAC to acenocoumar ol (58.6% to 70.2%). In switches from VKA to DOAC, ap ixaban was the most chosen (35.2%). Utilizing NLP and ML to extract RWD, we established the most comprehensive Spanish cohort of AF patients with O AC to date. Analysis revealed a high AF prevalence, patient complexity, and a ma rked VKA preference over DOAC."
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    Findings from Beijing Institute of Technology Update Knowledge of Machine Learni ng (Misdetect: Iterative Mislabel Detection Using Early Loss)

    42-42页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Beijing, People's Repub lic of China, by NewsRx journalists, research stated, "Supervised machine learni ng (ML) models trained on data with mislabeled instances often produce inaccurat e results due to label errors. Traditional methods of detecting mislabeled insta nces rely on data proximity, where an instance is considered mislabeled if its l abel is inconsistent with its neighbors." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key R&D Program of China, National Nat ural Science Foundation of China (NSFC), DITDP, National Science Foundation (NSF ), Beijing Natural Science Foundation, Research Funds of Renmin University of Ch ina. The news correspondents obtained a quote from the research from the Beijing Inst itute of Technology, "However, it often performs poorly, because an instance doe s not always share the same label with its neighbors. ML-based methods instead u tilize trainedmodels to differentiate between mislabeled and clean instances. Ho wever, these methods struggle to achieve high accuracy, since the models may hav e already overfitted mislabeled instances. In this paper, we propose a novel fra mework, MisDetect, that detects mislabeled instances during model training. MisD etect leverages the early loss observation to iteratively identify and remove mi slabeled instances. In this process, influence-based verification is applied to enhance the detection accuracy. Moreover, MisDetect automatically determines whe n the early loss is no longer effective in detecting mislabels such that the ite rative detection process should terminate. Finally, for the training instances t hat MisDetect is still not certain about whether they are mislabeled or not, Mis Detect automatically produces some pseudo labels to learn a binary classificatio n model and leverages the generalization ability of the machine learning model t o determine their status."
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    University of California Reports Findings in Artificial Intelligence (Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Method ology for Ligand Based and Structure Based Drug Design)

    43-43页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Berkeley, Califor nia, by NewsRx journalists, research stated, "Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with realtime 3D molecular do cking using AutoDock Vina, thereby simultaneously creating chemical novelty whil e constraining molecules for shape and molecular compatibility with target activ e sites." The news correspondents obtained a quote from the research from the University o f California, "Moreover, through the use of various types of reward functions, w e have introduced novelty in generative tasks for new molecules such as chemical similarity to a target ligand, molecules grown from known protein bound fragmen ts, and creation of molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflo w that filters out Pan-assay interference compounds, Lipinski rule violations, u ncommon structures in medicinal chemistry, and poor synthetic accessibility with options for cross-validation against other docking scoring functions and automa tion of a molecular dynamics simulation to measure pose stability. We also allow users to define a set of rules for the structures they would like to exclude du ring the training process and postfiltering steps."
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    University of Waterloo Reports Findings in Bladder Cancer (Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagno sis of Bladder Cancer)

    44-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Bladder Can cer is the subject of a report. According to news reporting originating in Water loo, Canada, by NewsRx journalists, research stated, "Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. How ever, the majority of the associated studies adopted biomarker-based unimodal an alysis to achieve cancer diagnosis, which has high false positives and low preci sion." The news reporters obtained a quote from the research from the University of Wat erloo, "Herein, we report a multimodal platform for the high-precision diagnosis of bladder cancer (BCa) through a multispectral 3D DNA machine in combination w ith a multimodal machine learning (ML) algorithm. The DNA machine was constructe d using magnetic microparticles (MNPs) functionalized with aptamers that specifi cally identify the target of interest, i.e., five protein markers on bladder-can cer-derived urinary EVs (uEVs). The aptamers were hybridized with DNA-stabilized silver nanoclusters (DNA/AgNCs) and a G-quadruplex/hemin complex to form a sens ing module. Such a DNA machine ensured multispectral detection of protein marker s by fluorescence (FL), inductively coupled plasma mass spectrometry (ICPMS), a nd UV-vis absorption (Abs). The obtained data sets then underwent uni- or multim odal ML for BCa diagnosis to compare the analytical performance. In this study, urine samples were obtained from our prospective cohort ( = 45). Our analytical results showed that the 3D DNA machine provided a detection limit of 9.2 x 10 pa rticles mL with a linear range of 4 x 10 to 5 x 10 particles mL for uEVs. Moreov er, the multimodal data fusion model exhibited an accuracy of 95.0% , a precision of 93.1%, and a recall rate of 93.2% on average, while those of the three types of unimodal models were no more than 91 %."
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    Researchers from Instituto de Investigacion Detail Research in Machine Learning (Recent advances of machine learning applications in the development of experime ntal homogeneous catalysis)

    45-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Logrono, Spain, by N ewsRx correspondents, research stated, "Machine Learning (ML) stands as a disrup tive technology, finding application across a diverse array of scientific discip lines." Financial supporters for this research include Agencia Estatal De Investigacion; Gobierno De Aragon; Fundacion Banco Santander. Our news correspondents obtained a quote from the research from Instituto de Inv estigacion: "When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimenta l iterations but also yields significant savings in time, resources, and waste g eneration. ML algorithms, often integrated with cheminformatic tools and quantum mechanics featurization, excel in predicting reaction outcomes that guide the e ngineering of catalysts for desired reactivity and selectivity."
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    Data on Prostatectomy Reported by Maria Chiara Sighinolfi and Colleagues (Cost a nalysis of new robotic competitors: a comparison of direct costs for initial hos pital stay between Da Vinci and Hugo RAS for radical prostatectomy)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery - Prostatectom y is the subject of a report. According to news reporting from Milan, Italy, by NewsRx journalists, research stated, "Robotic surgery with Da Vinci has revoluti onized the treatment of several diseases, including prostate cancer; nevertheles s, costs remain the major drawback. Recently, new robotic platforms entered the market aiming to reduce costs and improve the access to robotic surgery."
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    New Artificial Intelligence Findings Reported from Missouri University of Scienc e and Technology (Ai Composer Bias: Listeners Like Music Less When They Think It Was Composed By an Ai)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Artificial Intell igence are discussed in a new report. According to news reporting originating in Rolla, Missouri, by NewsRx journalists, research stated, "The use of artificial intelligence (AI) to compose music is becoming mainstream. Yet, there is a conc ern that listeners may have biases against AIs." Financial support for this research came from Missouri S&T Intellig ent Systems Center. The news reporters obtained a quote from the research from the Missouri Universi ty of Science and Technology, "Here, we test the hypothesis that listeners will like music less if they think it was composed by an AI. In Study 1, participants listened to excerpts of electronic and classical music and rated how much they liked the excerpts and whether they thought they were composed by an AI or human . Participants were more likely to attribute an AI composer to electronic music and liked music less that they thought was composed by an AI. In Study 2, we dir ectly manipulated composer identity by telling participants that the music they heard (electronic music) was composed by an AI or by a human, yet we found no ef fect of composer identity on liking. We hypothesized that this was due to the ‘A I-sounding' nature of electronic music. Therefore, in Study 3, we used a set of ‘human-sounding' classical music excerpts. Here, participants liked the music le ss when it was purportedly composed by an AI. We conclude with implications of t he AI composer bias for understanding perception of AIs in arts and aesthetic pr ocessing theories more broadly. Public Significance Statement Artificial intelli gence (AI)-computers making intelligent decisions or emulating humans-is revolut ionizing the music industry. Yet, very little is known about how people emotiona lly respond to AI-generated music."
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    Taizhou University Reports Findings in Artificial Intelligence (Colorectal cance r screening: The value of early detection and modern challenges)

    47-48页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Taizhou, People's Republic of China, by NewsRx journalists, research stated, "The screening of co lorectal cancer (CRC) is pivotal for both the prevention and treatment of this d isease, significantly improving early-stage tumor detection rates. This advancem ent not only boosts survival rates and quality of life for patients but also red uces the costs associated with treatment." The news correspondents obtained a quote from the research from Taizhou Universi ty, "However, the adoption of CRC screening methods faces numerous challenges, i ncluding the technical limitations of both noninvasive and invasive methods in t erms of sensitivity and specificity. Moreover, socioeconomic factors such as reg ional disparities, economic conditions, and varying levels of awareness affect s creening uptake. The coronavirus disease 2019 pandemic further intensified these cha-llenges, leading to reduced screening participation and increased waiting p eriods. Additionally, the growing prevalence of early-onset CRC necessitates inn ovative screening approaches. In response, research into new methodologies, incl uding artificial intelligence-based systems, aims to improve the precision and a ccessibility of screening. Proactive measures by governments and health organiza tions to enhance CRC screening efforts are underway, including increased advocac y, improved service delivery, and international cooperation. The role of technol ogical innovation and global health collaboration in advancing CRC screening is undeniable. Technologies such as artificial intelligence and gene sequencing are set to revolutionize CRC screening, making a significant impact on the fight ag ainst this disease."
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